HassanB4/warfarisk-baselines

Beyond MAE: Phase 1 Baselines

This repository contains the three mandatory Phase 1 baselines from the pipeline described in "Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models", a nine-phase reproducible ML pipeline for warfarin dose prediction on the public IWPC pharmacogenomic cohorts. These set the performance floor every later-phase model in the project is compared against; no model is reported as an improvement unless it beats the IWPC published equation below (MAE 9.177).

By: Hassan Barmandah, Omar Abdullah Bawazir, Siraj Aldeen Marghalani, Moath Shaat, Abdullah N. Alkattan, and Mariam M. AlEissa (corresponding author), Alfaisal University, Riyadh (with Umm Al-Qura University, Saudi Electronic University, Ministry of Health, Public Health Authority, King Khaled Eye Specialist Hospital Research Center, King Abdullah Petroleum Studies and Research Center)

This is a research artifact, not a validated clinical tool. It has not been evaluated prospectively and has no regulatory status.

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Model Description

Three baselines, evaluated on IWPC-6256 (n=6,037 after cleaning, n_test=1,207, fixed 80/20 patient-level split, seed 20260725):

1. Naive median-dose predictor: always outputs the training-fold median dose. The performance floor.
2. Clinical-only linear regression: ordinary least squares on clinical features only (age, weight, height, indication, comorbidity and comedication flags), no genetic information.
3. IWPC published pharmacogenetic equation: a hand-implemented version of the published square-root-of-dose formula (IWPC, N Engl J Med 2009;360:753-64), the only one of the three that uses genotype (CYP2C9/VKORC1).

Key Contributions

• Performance Floor: every model in this project's Phase 3 ablation is reported against these three baselines, not in isolation
• Genetics-Aware Reference: the IWPC equation baseline is the only one of the three that incorporates genotype, and beats the clinical-only baseline by the margin genetics would predict
• Provenance Discipline: the equation's coefficients are flagged as transcribed from the widely-cited public form (the same form implemented by public calculators such as warfarindosing.org), not verified directly against the original paper's Table 2; stated plainly rather than silently assumed correct

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🚀 How to Use

import joblib  # this repo's own artifact format (fit via common/hf_push.py);
                # only load .joblib files from this specific, trusted repo
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="HassanB4/warfarisk-baselines",
    filename="clinical_only_linear_regression.joblib",  # see repo files for exact names
)
model = joblib.load(model_path)
prediction = model.predict(your_clinical_features_dataframe)

The IWPC published-equation baseline is a plain Python function (iwpc_published_equation_predict()), not a fitted sklearn estimator; see HasanBGit/WarfaRisk's src/warfarisk/phase1_baselines.py for its implementation.

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⚙️ Training Procedure

Training Data

Split	Samples	Description
Training	4,830	80% of IWPC-6256, patient-ID-level split
Test	1,207	Held out, seed 20260725

Hyperparameters

Parameter	Value	Parameter	Value
Cohort	IWPC-6256	Split Level	Patient ID (not row)
Split Seed	20260725	Test Fraction	0.20
Leakage Audit	11/11 checks passed	Preprocessing Fit	Training fold only

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📊 Evaluation Results

Baseline	MAE (mg/week)	R²	PW20
Naive median dose	12.339	-0.048	0.342
Clinical-only linear regression	10.860	0.222	0.354
IWPC published pharmacogenetic equation	9.177	0.413	0.429

PW20 = proportion of predictions within 20% of the true dose (IWPC's own clinical-acceptability threshold is ≥0.50: none of these three baselines clear it; see HassanB4/warfarisk-autogluon-6256 for a model that gets closer).

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⚠️ Limitations

• Research-Only: not validated prospectively, no regulatory status
• Equation Provenance: the IWPC-equation baseline's coefficients were transcribed from the widely-cited public form, not independently verified against the original paper's Table 2
• Single Cohort: evaluated on IWPC-6256 only; see HassanB4/warfarisk-autogluon-1780 for the second cohort
• Fragmented Ancestry Labels: IWPC-6256's Black/African-American patients are split across three overlapping labels, which affects subgroup-level interpretation of any model trained on this cohort (addressed in this project's Phase 4/5 fairness and calibration analyses)

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🙏 Acknowledgements

We thank the PharmGKB / International Warfarin Pharmacogenetics Consortium for the IWPC dataset.

Related Links

• PharmGKB
• Code repository

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📜 Citation

This model is described in the following article, published in the Journal of Personalized Medicine.

Barmandah, H.; Bawazir, O.A.; Marghalani, S.A.; Shaat, M.; Alkattan, A.N.; AlEissa, M.M. Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models. J. Pers. Med. 2026, 16 (10), 531. https://doi.org/10.3390/jpm16100531

@Article{jpm16100531,
    author={Barmandah, Hassan and Bawazir, Omar A. and Marghalani, Siraj Aldeen and Shaat, Muath and Alkattan, Abdullah N. and AlEissa, Mariam M.},
    title={Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models},
    journal={Journal of Personalized Medicine},
    volume={16},
    year={2026},
    number={10},
    article-number={531},
    url={https://www.mdpi.com/2075-4426/16/10/531},
    issn={2075-4426},
    doi={10.3390/jpm16100531}
}

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📄 License

This project is licensed under the Apache 2.0 License. This model was fit on IWPC data; the fitted artifact and code are shared under Apache-2.0, but the underlying IWPC dataset is not redistributed by this repository; see HasanBGit/WarfaRisk's data/DATA.md for how to obtain it.